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Course Syllabus

Master 2021/2022

2D image processing

Type: Compulsory course (Master of Computer Vision)
Area of studies: Applied Mathematics and Informatics
Delivered by: Department of Applied Mathematics and Informatics
Where: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)
When: 1 year, 2 module
Mode of studies: distance learning
Open to: students of one campus
Instructors: Andrey Savchenko
Master’s programme: Master of Computer Vision
Language: English
ECTS credits: 7
Contact hours: 2

Course Syllabus

Full Syllabus

Abstract

The course is devoted to the usage of computer vision libraries like OpenCV in 2d image processing. The course includes sections of image filtering and thresholding, edge/corner/interest point detection, local and global descriptors, video tracking.
Learning Objectives

Learning Objectives

  • Learning the main algorithms of traditional image processing.
  • Thorough understanding of benefits and limitations of traditional image processing.
Expected Learning Outcomes

Expected Learning Outcomes

  • Apply image binarization techniques.
  • Apply local filters for image smoothing.
  • Apply object detection, image retrieval and/or image segmentation in practice.
  • Apply point-wise operations for image contrast enhancements.
  • Create panoramas with image stitching.
  • Detect objects with the Viola-Jones method.
  • Display video from web-camera and video files.
  • Distinguish image color models.
  • Distinguish the main tasks of computer vision and 2d image processing.
  • Find correspondent parts in different images.
  • Perform denoising in binary images with mathematical morphology.
  • Perform image processing operations for video frames.
  • Process grayscale and color images.
  • Segment objects in binary images.
  • Set-up environment (OpenCV library) for C++/Python development on Windows and Linux/MacOS.
  • Solve content-based image retrieval tasks.
  • Track object movements in videos.
  • Use FFT (Fast Fourier transform) for image filtering.
Course Contents

Course Contents

  • 2D image processing overview
  • Basic operations of 2D image processing
  • Local (spatial) image filtering
  • Image matching and local descriptors
  • Image classification and object detection
  • 2D image segmentation and object tracking. Final project
Assessment Elements

Assessment Elements

  • non-blocking Weekly Quizzes
  • non-blocking Programming Assignment 2 per course
  • non-blocking Final exam
    Testing assignment Programming assignment
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.4 * Final exam + 0.2 * Weekly Quizzes + 0.4 * Programming Assignment 2 per course
Bibliography

Bibliography

Recommended Core Bibliography

  • Prince, S. J. D. (2012). Computer Vision : Models, Learning, and Inference. New York: Cambridge eText. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=458656

Recommended Additional Bibliography

  • Richard Szeliski. (2010). Computer Vision: Algorithms and Applications. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C0E46D49